Answer: K-means clustering

## Explanation K-means clustering is the most suitable machine learning model for this task because: - **Clustering Purpose**: K-means is specifically designed for grouping data points into distinct clusters based on similarity - **Fixed Number of Groups**: The requirement to split customers into exactly 20 distinct groups aligns perfectly with K-means, where you specify the number of clusters (k=20) - **Customer Segmentation**: This is a classic use case for clustering algorithms in customer analytics - **Scalability**: K-means can efficiently handle 2,000 data points **Why other options are less suitable:** - **Linear Regression**: Used for predicting continuous outcomes, not grouping - **Logistic Regression**: Used for binary classification, not multi-group clustering - **Decision Tree**: Primarily for classification or regression, though can be used for clustering in some variations - **Support Vector Machine**: Mainly for classification, though SVM clustering exists but is less common - **Neural Network**: Can be used for clustering but is more complex and computationally intensive than K-means K-means clustering is the standard and most appropriate choice for this customer segmentation task.

Author: Tanishq Prabhu